Tod

class minkasi_wrapper.extern.minkasi.minkasi.Tod(info)

Bases: object

Methods Summary

apply_noise([dat])
apply_noise_cm_white([dat])
apply_noise_white_masked([dat])
clear_saved_pix([tag])
copy([copy_info])
cut_detectors(isgood)
dot(mapset, mapset_out[, times])
get_data()
get_data_dims()
get_det_weights()
get_empty([clear])
get_ndata()
get_ndet()
get_nsamp()
get_radec()
get_saved_pix([tag])
get_tvec()
lims()
mapset2tod(mapset[, dat])
prior_from_skymap(skymap)	stuff.
save_pixellization(tag, ipix)
set_apix()	calculates dxel normalized to +-1 from elevation
set_jumps(jumps)
set_noise([modelclass, dat, delayed])
set_noise_binned_eig([dat, freqs, ...])
set_noise_cm_white()
set_noise_smoothed_svd([fwhm, func, pars, ...])	If func comes in as not empty, assume we can call func(pars,tod) to get a predicted model for the tod that we subtract off before estimating the noise.
set_noise_white_masked()
set_pix(map)
set_tag(tag)
timestream_chisq([dat])
tod2mapset(mapset[, dat])

Methods Documentation

apply_noise(dat=None)

apply_noise_cm_white(dat=None)

apply_noise_white_masked(dat=None)

clear_saved_pix(tag=None)

copy(copy_info=False)

cut_detectors(isgood)

dot(mapset, mapset_out, times=False)

get_data()

get_data_dims()

get_det_weights()

get_empty(clear=False)

get_ndata()

get_ndet()

get_nsamp()

get_radec()

get_saved_pix(tag=None)

get_tvec()

lims()

mapset2tod(mapset, dat=None)

prior_from_skymap(skymap)

stuff. prior_from_skymap(self,skymap): Given e.g. the gradient of a map that has been zeroed under some threshold, return a CutsCompact object that can be used as a prior for solving for per-sample deviations due to strong map gradients. This is to reduce X’s around bright sources. The input map should be a SkyMap that is non-zero where one wishes to solve for the per-sample deviations, and the non-zero values should be the standard deviations expected in those pixel. The returned CutsCompact object will have the weight (i.e. 1/input squared) in its map.

save_pixellization(tag, ipix)

set_apix()

calculates dxel normalized to +-1 from elevation

set_jumps(jumps)

set_noise(modelclass=<class 'minkasi_wrapper.extern.minkasi.minkasi.NoiseSmoothedSVD'>, dat=None, delayed=False, *args, **kwargs)

set_noise_binned_eig(dat=None, freqs=None, scale_facs=None, thresh=5.0)

set_noise_cm_white()

set_noise_smoothed_svd(fwhm=50, func=None, pars=None, prewhiten=False, fit_powlaw=False)

If func comes in as not empty, assume we can call func(pars,tod) to get a predicted model for the tod that we subtract off before estimating the noise.

set_noise_white_masked()

set_pix(map)

set_tag(tag)

timestream_chisq(dat=None)

tod2mapset(mapset, dat=None)